Joint operation optimization for electric vehicles(EVs)and on-site or adjacent photovoltaic generation(PVG)are pivotal to maintaining the security and economics of the operation of the power system concerned.Conventio...Joint operation optimization for electric vehicles(EVs)and on-site or adjacent photovoltaic generation(PVG)are pivotal to maintaining the security and economics of the operation of the power system concerned.Conventional offline optimization algorithms lack real-time applicability due to uncertainties involved in the charging service of an EV charging station(EVCS).Firstly,an optimization model for real-time EV charging strategy is proposed to address these challenges,which accounts for environmental uncertainties of an EVCS,encompassing EV arrivals,charging demands,PVG outputs,and the electricity price.Then,a scenario-based two-stage optimization approach is formulated.The scenarios of the underlying uncertain environmental factors are generated by the Bayesian long short-term memory(B-LSTM)network.Finally,numerical results substantiate the efficacy of the proposed optimization approach,and demonstrate superior profitability compared with prevalent approaches.展开更多
基金supported in part by the National Natural Science Foundation of China(No.U1910216)in part by the Science and Technology Project of China Southern Power Grid Company Limited(No.080037KK52190039/GZHKJXM20190100)。
文摘Joint operation optimization for electric vehicles(EVs)and on-site or adjacent photovoltaic generation(PVG)are pivotal to maintaining the security and economics of the operation of the power system concerned.Conventional offline optimization algorithms lack real-time applicability due to uncertainties involved in the charging service of an EV charging station(EVCS).Firstly,an optimization model for real-time EV charging strategy is proposed to address these challenges,which accounts for environmental uncertainties of an EVCS,encompassing EV arrivals,charging demands,PVG outputs,and the electricity price.Then,a scenario-based two-stage optimization approach is formulated.The scenarios of the underlying uncertain environmental factors are generated by the Bayesian long short-term memory(B-LSTM)network.Finally,numerical results substantiate the efficacy of the proposed optimization approach,and demonstrate superior profitability compared with prevalent approaches.
